Light influences most ecosystems on earth, from sun-dappled forests to bioluminescent creatures in the ocean deep. Biologists have long studied nano- and micro-scale organismal adaptations to manipulate light using ever-more sophisticated microscopy, spectroscopy, and other analytical equipment. In combination with experimental tools, simulations of light interacting with objects can help researchers determine the impact of observed structures and explore how variations affect optical function. In particular, the finite-difference time-domain (FDTD) method is widely used throughout the nanophotonics community to efficiently simulate light interacting with a variety of materials and optical devices. More recently, FDTD has been used to characterize optical adaptations in nature, such as camouflage in fish and other organisms, colors in sexually-selected birds and spiders, and photosynthetic efficiency in plants. FDTD is also common in bioengineering, as the design of biologically-inspired engineered structures can be guided and optimized through FDTD simulations. Parameter sweeps are a particularly useful application of FDTD, which allows researchers to explore a range of variables and modifications in natural and synthetic systems (e.g., to investigate the optical effects of changing the sizes, shape, or refractive indices of a structure). Here, we review the use of FDTD simulations in biology and present a brief methods primer tailored for life scientists, with a focus on the commercially available software Lumerical FDTD. We give special attention to whether FDTD is the right tool to use, how experimental techniques are used to acquire and import the structures of interest, and how their optical properties such as refractive index and absorption are obtained. This primer is intended to help researchers understand FDTD, implement the method to model optical effects, and learn about the benefits and limitations of this tool. Altogether, FDTD is well-suited to (i) characterize optical adaptations and (ii) provide mechanistic explanations; by doing so, it helps (iii) make conclusions about evolutionary theory and (iv) inspire new technologies based on natural structures.